Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations269952
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.4 MiB
Average record size in memory192.0 B

Variable types

Categorical10
Numeric13

Alerts

LNG is highly overall correlated with ZIPHigh correlation
ZIP is highly overall correlated with LNGHigh correlation
baths is highly overall correlated with beds and 1 other fieldsHigh correlation
beds is highly overall correlated with baths and 1 other fieldsHigh correlation
beds_was_null is highly overall correlated with propertyType and 1 other fieldsHigh correlation
propertyType is highly overall correlated with beds_was_nullHigh correlation
sqft is highly overall correlated with baths and 1 other fieldsHigh correlation
status is highly overall correlated with beds_was_nullHigh correlation
status is highly imbalanced (54.5%) Imbalance
cooling is highly imbalanced (55.6%) Imbalance
lotsize is highly skewed (γ1 = 29.07257037) Skewed
mean_school_distance is highly skewed (γ1 = 145.1541446) Skewed

Reproduction

Analysis started2024-11-26 16:08:18.711581
Analysis finished2024-11-26 16:08:57.616602
Duration38.91 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

status
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
for sale
177652 
active
68666 
other
 
5077
foreclosed
 
4544
new construction
 
4480
Other values (4)
 
9533

Length

Max length22
Median length8
Mean length7.7501037
Min length5

Characters and Unicode

Total characters2092156
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowactive
2nd rowfor sale
3rd rowfor sale
4th rowactive
5th rowactive

Common Values

ValueCountFrequency (%)
for sale 177652
65.8%
active 68666
 
25.4%
other 5077
 
1.9%
foreclosed 4544
 
1.7%
new construction 4480
 
1.7%
pending 3894
 
1.4%
pre-foreclosed 2359
 
0.9%
under contract showing 2213
 
0.8%
auction 1067
 
0.4%

Length

2024-11-26T19:08:57.894170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:08:58.138361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
for 177652
38.9%
sale 177652
38.9%
active 68666
 
15.0%
other 5077
 
1.1%
foreclosed 4544
 
1.0%
new 4480
 
1.0%
construction 4480
 
1.0%
pending 3894
 
0.9%
pre-foreclosed 2359
 
0.5%
under 2213
 
0.5%
Other values (3) 5493
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 278147
13.3%
a 249598
11.9%
o 210988
10.1%
r 200897
9.6%
s 191248
9.1%
186558
8.9%
f 184555
8.8%
l 184555
8.8%
c 90022
 
4.3%
t 88196
 
4.2%
Other values (10) 227392
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1903239
91.0%
Space Separator 186558
 
8.9%
Dash Punctuation 2359
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 278147
14.6%
a 249598
13.1%
o 210988
11.1%
r 200897
10.6%
s 191248
10.0%
f 184555
9.7%
l 184555
9.7%
c 90022
 
4.7%
t 88196
 
4.6%
i 80320
 
4.2%
Other values (8) 144713
7.6%
Space Separator
ValueCountFrequency (%)
186558
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1903239
91.0%
Common 188917
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 278147
14.6%
a 249598
13.1%
o 210988
11.1%
r 200897
10.6%
s 191248
10.0%
f 184555
9.7%
l 184555
9.7%
c 90022
 
4.7%
t 88196
 
4.6%
i 80320
 
4.2%
Other values (8) 144713
7.6%
Common
ValueCountFrequency (%)
186558
98.8%
- 2359
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2092156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 278147
13.3%
a 249598
11.9%
o 210988
10.1%
r 200897
9.6%
s 191248
9.1%
186558
8.9%
f 184555
8.8%
l 184555
8.8%
c 90022
 
4.3%
t 88196
 
4.2%
Other values (10) 227392
10.9%

propertyType
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
single-family
176944 
condo
41831 
townhouse
 
16237
other
 
14130
multi-family
 
6965
Other values (4)
 
13845

Length

Max length13
Median length13
Mean length10.930202
Min length4

Characters and Unicode

Total characters2950630
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle-family
2nd rowsingle-family
3rd rowtownhouse
4th rowother
5th rowsingle-family

Common Values

ValueCountFrequency (%)
single-family 176944
65.5%
condo 41831
 
15.5%
townhouse 16237
 
6.0%
other 14130
 
5.2%
multi-family 6965
 
2.6%
traditional 6487
 
2.4%
contemporary 2741
 
1.0%
mobile home 2589
 
1.0%
coop 2028
 
0.8%

Length

2024-11-26T19:08:58.295037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:08:58.427558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
single-family 176944
64.9%
condo 41831
 
15.3%
townhouse 16237
 
6.0%
other 14130
 
5.2%
multi-family 6965
 
2.6%
traditional 6487
 
2.4%
contemporary 2741
 
1.0%
mobile 2589
 
0.9%
home 2589
 
0.9%
coop 2028
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 383381
13.0%
l 376894
12.8%
n 244240
8.3%
e 215230
 
7.3%
a 199624
 
6.8%
m 198793
 
6.7%
s 193181
 
6.5%
y 186650
 
6.3%
- 183909
 
6.2%
f 183909
 
6.2%
Other values (12) 584819
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2764132
93.7%
Dash Punctuation 183909
 
6.2%
Space Separator 2589
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 383381
13.9%
l 376894
13.6%
n 244240
8.8%
e 215230
7.8%
a 199624
7.2%
m 198793
7.2%
s 193181
7.0%
y 186650
6.8%
f 183909
6.7%
g 176944
6.4%
Other values (10) 405286
14.7%
Dash Punctuation
ValueCountFrequency (%)
- 183909
100.0%
Space Separator
ValueCountFrequency (%)
2589
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2764132
93.7%
Common 186498
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 383381
13.9%
l 376894
13.6%
n 244240
8.8%
e 215230
7.8%
a 199624
7.2%
m 198793
7.2%
s 193181
7.0%
y 186650
6.8%
f 183909
6.7%
g 176944
6.4%
Other values (10) 405286
14.7%
Common
ValueCountFrequency (%)
- 183909
98.6%
2589
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2950630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 383381
13.0%
l 376894
12.8%
n 244240
8.3%
e 215230
 
7.3%
a 199624
 
6.8%
m 198793
 
6.7%
s 193181
 
6.5%
y 186650
 
6.3%
- 183909
 
6.2%
f 183909
 
6.2%
Other values (12) 584819
19.8%

baths
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4929247
Minimum0
Maximum30
Zeros54
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:08:58.560748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86756602
Coefficient of variation (CV)0.34801133
Kurtosis18.903746
Mean2.4929247
Median Absolute Deviation (MAD)0.5
Skewness1.9994376
Sum672970
Variance0.7526708
MonotonicityNot monotonic
2024-11-26T19:08:58.722419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2 114096
42.3%
3 61060
22.6%
2.5 35327
 
13.1%
4 19240
 
7.1%
1 15039
 
5.6%
2.25 5711
 
2.1%
3.5 5170
 
1.9%
5 4459
 
1.7%
1.5 3637
 
1.3%
1.75 1457
 
0.5%
Other values (33) 4756
 
1.8%
ValueCountFrequency (%)
0 54
 
< 0.1%
0.75 202
 
0.1%
1 15039
 
5.6%
1.25 921
 
0.3%
1.5 3637
 
1.3%
1.75 1457
 
0.5%
2 114096
42.3%
2.25 5711
 
2.1%
2.5 35327
 
13.1%
2.75 771
 
0.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 4
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 4
< 0.1%
15 2
< 0.1%
14 3
< 0.1%

fireplace
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
190834 
1
79118 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters269952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

Length

2024-11-26T19:08:58.878534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:08:58.990104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

Most occurring characters

ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 269952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 190834
70.7%
1 79118
29.3%

sqft
Real number (ℝ)

High correlation 

Distinct6549
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1933.299
Minimum121
Maximum7920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:08:59.359283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile796
Q11263
median1754
Q32405
95-th percentile3664
Maximum7920
Range7799
Interquartile range (IQR)1142

Descriptive statistics

Standard deviation920.21482
Coefficient of variation (CV)0.47598164
Kurtosis2.6196629
Mean1933.299
Median Absolute Deviation (MAD)550
Skewness1.2731985
Sum5.2189792 × 108
Variance846795.31
MonotonicityNot monotonic
2024-11-26T19:08:59.531648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 1254
 
0.5%
1000 924
 
0.3%
1500 890
 
0.3%
1800 867
 
0.3%
1100 831
 
0.3%
1400 821
 
0.3%
1600 750
 
0.3%
2000 718
 
0.3%
800 666
 
0.2%
1300 665
 
0.2%
Other values (6539) 261566
96.9%
ValueCountFrequency (%)
121 1
 
< 0.1%
122 1
 
< 0.1%
130 2
< 0.1%
144 3
< 0.1%
146 1
 
< 0.1%
147 2
< 0.1%
150 2
< 0.1%
151 1
 
< 0.1%
160 1
 
< 0.1%
169 1
 
< 0.1%
ValueCountFrequency (%)
7920 2
< 0.1%
7892 1
< 0.1%
7886 1
< 0.1%
7875 1
< 0.1%
7863 1
< 0.1%
7846 1
< 0.1%
7837 1
< 0.1%
7816 1
< 0.1%
7814 1
< 0.1%
7810 1
< 0.1%

beds
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1645181
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:08:59.684393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum44
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98548918
Coefficient of variation (CV)0.3114184
Kurtosis24.290393
Mean3.1645181
Median Absolute Deviation (MAD)0
Skewness1.8085545
Sum854268
Variance0.97118892
MonotonicityNot monotonic
2024-11-26T19:08:59.795416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 136035
50.4%
4 57628
21.3%
2 50627
 
18.8%
5 15568
 
5.8%
1 5489
 
2.0%
6 3078
 
1.1%
7 660
 
0.2%
8 477
 
0.2%
9 167
 
0.1%
10 102
 
< 0.1%
Other values (12) 121
 
< 0.1%
ValueCountFrequency (%)
1 5489
 
2.0%
2 50627
 
18.8%
3 136035
50.4%
4 57628
21.3%
5 15568
 
5.8%
6 3078
 
1.1%
7 660
 
0.2%
8 477
 
0.2%
9 167
 
0.1%
10 102
 
< 0.1%
ValueCountFrequency (%)
44 1
 
< 0.1%
28 2
 
< 0.1%
24 3
 
< 0.1%
22 1
 
< 0.1%
20 3
 
< 0.1%
18 3
 
< 0.1%
16 8
< 0.1%
15 2
 
< 0.1%
14 10
< 0.1%
13 11
< 0.1%

stories
Real number (ℝ)

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8150608
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:08:59.920390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum70
Range69
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7889877
Coefficient of variation (CV)0.9856351
Kurtosis230.35797
Mean1.8150608
Median Absolute Deviation (MAD)0
Skewness11.070462
Sum489979.3
Variance3.2004769
MonotonicityNot monotonic
2024-11-26T19:09:00.092666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 149452
55.4%
2 79845
29.6%
4 20342
 
7.5%
3 12941
 
4.8%
9 2644
 
1.0%
6 1574
 
0.6%
1.5 1011
 
0.4%
5 627
 
0.2%
8 261
 
0.1%
7 258
 
0.1%
Other values (58) 997
 
0.4%
ValueCountFrequency (%)
1 149452
55.4%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.5 1011
 
0.4%
1.7 13
 
< 0.1%
1.75 18
 
< 0.1%
2 79845
29.6%
2.2 1
 
< 0.1%
2.5 133
 
< 0.1%
3 12941
 
4.8%
ValueCountFrequency (%)
70 1
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
60 5
< 0.1%
58 1
 
< 0.1%
57 3
< 0.1%
56 4
< 0.1%
55 4
< 0.1%
54 2
 
< 0.1%

target
Real number (ℝ)

Distinct26898
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364955.86
Minimum48550
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:00.245582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48550
5-th percentile99900
Q1209500
median310000
Q3475000
95-th percentile815000
Maximum1000000
Range951450
Interquartile range (IQR)265500

Descriptive statistics

Standard deviation213848.33
Coefficient of variation (CV)0.58595668
Kurtosis0.37830086
Mean364955.86
Median Absolute Deviation (MAD)122512
Skewness0.97725532
Sum9.8520565 × 1010
Variance4.5731107 × 1010
MonotonicityNot monotonic
2024-11-26T19:09:00.417515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225000 1483
 
0.5%
249900 1387
 
0.5%
299900 1384
 
0.5%
275000 1376
 
0.5%
325000 1343
 
0.5%
399000 1342
 
0.5%
350000 1337
 
0.5%
250000 1276
 
0.5%
199900 1270
 
0.5%
375000 1247
 
0.5%
Other values (26888) 256507
95.0%
ValueCountFrequency (%)
48550 1
< 0.1%
48594 1
< 0.1%
48600 1
< 0.1%
48630 1
< 0.1%
48640 1
< 0.1%
48684 1
< 0.1%
48700 1
< 0.1%
48702 2
< 0.1%
48710 1
< 0.1%
48732 1
< 0.1%
ValueCountFrequency (%)
1000000 228
0.1%
999999 131
< 0.1%
999998 7
 
< 0.1%
999996 1
 
< 0.1%
999995 11
 
< 0.1%
999990 6
 
< 0.1%
999977 1
 
< 0.1%
999950 26
 
< 0.1%
999900 132
< 0.1%
999888 5
 
< 0.1%

ZIP
Real number (ℝ)

High correlation 

Distinct4005
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53267.61
Minimum1104
Maximum99338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:00.572474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1104
5-th percentile19127
Q133069
median37419
Q377845
95-th percentile95823
Maximum99338
Range98234
Interquartile range (IQR)44776

Descriptive statistics

Standard deviation26263.119
Coefficient of variation (CV)0.49304106
Kurtosis-1.4232834
Mean53267.61
Median Absolute Deviation (MAD)17401
Skewness0.24786373
Sum1.4379698 × 1010
Variance6.8975142 × 108
MonotonicityNot monotonic
2024-11-26T19:09:00.737283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78245 1351
 
0.5%
33131 1237
 
0.5%
34747 1233
 
0.5%
78253 1226
 
0.5%
78254 1206
 
0.4%
34746 1056
 
0.4%
32137 1050
 
0.4%
33132 1004
 
0.4%
33130 983
 
0.4%
33137 908
 
0.3%
Other values (3995) 258698
95.8%
ValueCountFrequency (%)
1104 10
< 0.1%
1105 4
 
< 0.1%
1107 2
 
< 0.1%
1108 12
< 0.1%
1109 14
< 0.1%
1118 7
< 0.1%
1119 6
< 0.1%
1128 3
 
< 0.1%
1129 6
< 0.1%
1151 5
 
< 0.1%
ValueCountFrequency (%)
99338 83
< 0.1%
99337 128
< 0.1%
99336 109
< 0.1%
99224 71
< 0.1%
99223 79
< 0.1%
99218 27
 
< 0.1%
99217 60
 
< 0.1%
99216 22
 
< 0.1%
99212 43
 
< 0.1%
99208 150
0.1%

LAT
Real number (ℝ)

Distinct3982
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.259243
Minimum25.557912
Maximum48.798606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:00.899665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25.557912
5-th percentile25.834607
Q128.586751
median32.63587
Q336.751519
95-th percentile43.12778
Maximum48.798606
Range23.240694
Interquartile range (IQR)8.164768

Descriptive statistics

Standard deviation5.8153006
Coefficient of variation (CV)0.17484766
Kurtosis-0.47664519
Mean33.259243
Median Absolute Deviation (MAD)4.049119
Skewness0.6414332
Sum8978399.1
Variance33.817721
MonotonicityNot monotonic
2024-11-26T19:09:01.049741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.401093 1351
 
0.5%
25.766206 1237
 
0.5%
28.311495 1233
 
0.5%
29.469011 1226
 
0.5%
29.539126 1206
 
0.4%
36.751519 1133
 
0.4%
28.238826 1056
 
0.4%
29.581507 1050
 
0.4%
25.77789 1004
 
0.4%
25.768524 983
 
0.4%
Other values (3972) 258473
95.7%
ValueCountFrequency (%)
25.557912 148
 
0.1%
25.560027 60
 
< 0.1%
25.572213 183
 
0.1%
25.595896 182
 
0.1%
25.596129 301
0.1%
25.606126 482
0.2%
25.63884 49
 
< 0.1%
25.652131 261
0.1%
25.654426 395
0.1%
25.659873 297
0.1%
ValueCountFrequency (%)
48.798606 82
< 0.1%
48.75094 33
 
< 0.1%
48.696127 81
< 0.1%
48.216792 3
 
< 0.1%
48.089968 59
< 0.1%
48.056723 137
0.1%
48.006311 56
< 0.1%
47.955367 34
 
< 0.1%
47.948393 5
 
< 0.1%
47.945519 93
< 0.1%

LNG
Real number (ℝ)

High correlation 

Distinct3982
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.045875
Minimum-123.42537
Maximum-69.573012
Zeros0
Zeros (%)0.0%
Negative269952
Negative (%)100.0%
Memory size4.1 MiB
2024-11-26T19:09:01.219263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-123.42537
5-th percentile-121.80075
Q1-97.546399
median-84.544149
Q3-80.797285
95-th percentile-75.217795
Maximum-69.573012
Range53.852357
Interquartile range (IQR)16.749114

Descriptive statistics

Standard deviation13.700993
Coefficient of variation (CV)-0.1504845
Kurtosis-0.085136656
Mean-91.045875
Median Absolute Deviation (MAD)5.723976
Skewness-0.99177023
Sum-24578016
Variance187.7172
MonotonicityNot monotonic
2024-11-26T19:09:01.386214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.730806 1351
 
0.5%
-80.182897 1237
 
0.5%
-81.595565 1233
 
0.5%
-98.797801 1226
 
0.5%
-98.725885 1206
 
0.4%
-119.680602 1133
 
0.4%
-81.445346 1056
 
0.4%
-81.218196 1050
 
0.4%
-80.176165 1004
 
0.4%
-80.203359 983
 
0.4%
Other values (3972) 258473
95.7%
ValueCountFrequency (%)
-123.425369 11
 
< 0.1%
-123.230905 16
< 0.1%
-123.192759 9
 
< 0.1%
-123.133235 4
 
< 0.1%
-123.111691 17
< 0.1%
-123.094751 21
< 0.1%
-123.080181 6
 
< 0.1%
-123.064528 24
< 0.1%
-123.063616 39
< 0.1%
-123.058241 2
 
< 0.1%
ValueCountFrequency (%)
-69.573012 1
 
< 0.1%
-69.661276 32
< 0.1%
-69.843372 18
< 0.1%
-69.887309 39
< 0.1%
-70.231801 2
 
< 0.1%
-70.298093 12
 
< 0.1%
-70.31253 3
 
< 0.1%
-70.346877 4
 
< 0.1%
-70.36307 1
 
< 0.1%
-70.392013 2
 
< 0.1%

private_pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
237155 
1
32797 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters269952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

Length

2024-11-26T19:09:01.537936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:01.643639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 269952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 237155
87.9%
1 32797
 
12.1%

year_built
Real number (ℝ)

Distinct122
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1982.6954
Minimum1900
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:01.762662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1923
Q11961
median1988
Q32007
95-th percentile2019
Maximum2021
Range121
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.434063
Coefficient of variation (CV)0.015349843
Kurtosis-0.39833819
Mean1982.6954
Median Absolute Deviation (MAD)22
Skewness-0.69420134
Sum5.3523259 × 108
Variance926.2322
MonotonicityNot monotonic
2024-11-26T19:09:01.929380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 26854
 
9.9%
2006 7707
 
2.9%
2005 7489
 
2.8%
2007 6887
 
2.6%
2018 5924
 
2.2%
2004 5310
 
2.0%
2017 4796
 
1.8%
2016 4706
 
1.7%
2003 4539
 
1.7%
2008 4471
 
1.7%
Other values (112) 191269
70.9%
ValueCountFrequency (%)
1900 1760
0.7%
1901 291
 
0.1%
1902 91
 
< 0.1%
1903 109
 
< 0.1%
1904 150
 
0.1%
1905 493
 
0.2%
1906 207
 
0.1%
1907 221
 
0.1%
1908 321
 
0.1%
1909 257
 
0.1%
ValueCountFrequency (%)
2021 37
 
< 0.1%
2020 1873
 
0.7%
2019 26854
9.9%
2018 5924
 
2.2%
2017 4796
 
1.8%
2016 4706
 
1.7%
2015 3745
 
1.4%
2014 3014
 
1.1%
2013 2337
 
0.9%
2012 1670
 
0.6%

heating
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
forced air
115166 
no data
59556 
central
29087 
other
28286 
electric
 
10305
Other values (5)
27552 

Length

Max length14
Median length10
Mean length8.0807032
Min length3

Characters and Unicode

Total characters2181402
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcentral
2nd rowno data
3rd rowforced air
4th rowelectric
5th rowno data

Common Values

ValueCountFrequency (%)
forced air 115166
42.7%
no data 59556
22.1%
central 29087
 
10.8%
other 28286
 
10.5%
electric 10305
 
3.8%
gas 9732
 
3.6%
heat pump 8593
 
3.2%
baseboard 3684
 
1.4%
wall 3192
 
1.2%
heating system 2351
 
0.9%

Length

2024-11-26T19:09:02.085552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:02.212764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
forced 115166
25.3%
air 115166
25.3%
no 59556
13.1%
data 59556
13.1%
central 29087
 
6.4%
other 28286
 
6.2%
electric 10305
 
2.3%
gas 9732
 
2.1%
heat 8593
 
1.9%
pump 8593
 
1.9%
Other values (4) 11578
 
2.5%

Most occurring characters

ValueCountFrequency (%)
r 301694
13.8%
a 294601
13.5%
e 210128
9.6%
o 206692
9.5%
185666
8.5%
d 178406
8.2%
c 164863
7.6%
t 140529
6.4%
i 127822
5.9%
f 115166
 
5.3%
Other values (11) 255835
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1995736
91.5%
Space Separator 185666
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 301694
15.1%
a 294601
14.8%
e 210128
10.5%
o 206692
10.4%
d 178406
8.9%
c 164863
8.3%
t 140529
7.0%
i 127822
6.4%
f 115166
 
5.8%
n 90994
 
4.6%
Other values (10) 164841
8.3%
Space Separator
ValueCountFrequency (%)
185666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1995736
91.5%
Common 185666
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 301694
15.1%
a 294601
14.8%
e 210128
10.5%
o 206692
10.4%
d 178406
8.9%
c 164863
8.3%
t 140529
7.0%
i 127822
6.4%
f 115166
 
5.8%
n 90994
 
4.6%
Other values (10) 164841
8.3%
Common
ValueCountFrequency (%)
185666
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2181402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 301694
13.8%
a 294601
13.5%
e 210128
9.6%
o 206692
9.5%
185666
8.5%
d 178406
8.2%
c 164863
7.6%
t 140529
6.4%
i 127822
5.9%
f 115166
 
5.3%
Other values (11) 255835
11.7%

cooling
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
central
171714 
no data
72471 
cooling system
 
9761
other
 
6383
wall
 
3936
Other values (5)
 
5687

Length

Max length14
Median length7
Mean length7.247481
Min length4

Characters and Unicode

Total characters1956472
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno data
2nd rowno data
3rd rowcentral
4th rowcentral
5th rowno data

Common Values

ValueCountFrequency (%)
central 171714
63.6%
no data 72471
26.8%
cooling system 9761
 
3.6%
other 6383
 
2.4%
wall 3936
 
1.5%
refrigeration 2105
 
0.8%
electric 1301
 
0.5%
ceiling fan 1036
 
0.4%
evaporative 939
 
0.3%
has heating 306
 
0.1%

Length

2024-11-26T19:09:02.390031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:02.527493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central 171714
48.6%
no 72471
20.5%
data 72471
20.5%
cooling 9761
 
2.8%
system 9761
 
2.8%
other 6383
 
1.8%
wall 3936
 
1.1%
refrigeration 2105
 
0.6%
electric 1301
 
0.4%
ceiling 1036
 
0.3%
Other values (4) 2587
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 326223
16.7%
t 264980
13.5%
n 258429
13.2%
e 197890
10.1%
l 191684
9.8%
r 186652
9.5%
c 185113
9.5%
o 101420
 
5.2%
83574
 
4.3%
d 72471
 
3.7%
Other values (10) 88036
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1872898
95.7%
Space Separator 83574
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 326223
17.4%
t 264980
14.1%
n 258429
13.8%
e 197890
10.6%
l 191684
10.2%
r 186652
10.0%
c 185113
9.9%
o 101420
 
5.4%
d 72471
 
3.9%
s 19828
 
1.1%
Other values (9) 68208
 
3.6%
Space Separator
ValueCountFrequency (%)
83574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1872898
95.7%
Common 83574
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 326223
17.4%
t 264980
14.1%
n 258429
13.8%
e 197890
10.6%
l 191684
10.2%
r 186652
10.0%
c 185113
9.9%
o 101420
 
5.4%
d 72471
 
3.9%
s 19828
 
1.1%
Other values (9) 68208
 
3.6%
Common
ValueCountFrequency (%)
83574
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1956472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 326223
16.7%
t 264980
13.5%
n 258429
13.2%
e 197890
10.1%
l 191684
9.8%
r 186652
9.5%
c 185113
9.5%
o 101420
 
5.2%
83574
 
4.3%
d 72471
 
3.7%
Other values (10) 88036
 
4.5%

parking
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
no data
116111 
attached garage
63118 
2 spaces
27292 
1 space
14968 
detached garage
 
11144
Other values (10)
37319 

Length

Max length15
Median length7
Mean length9.3712623
Min length4

Characters and Unicode

Total characters2529791
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno data
2nd rowno data
3rd rowno data
4th rowno data
5th rowno data

Common Values

ValueCountFrequency (%)
no data 116111
43.0%
attached garage 63118
23.4%
2 spaces 27292
 
10.1%
1 space 14968
 
5.5%
detached garage 11144
 
4.1%
carport 10022
 
3.7%
off street 6144
 
2.3%
other 4451
 
1.6%
3 spaces 3917
 
1.5%
on street 3309
 
1.2%
Other values (5) 9476
 
3.5%

Length

2024-11-26T19:09:02.710443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 116111
22.3%
data 116111
22.3%
garage 75925
14.6%
attached 63118
12.1%
spaces 35154
 
6.8%
2 27292
 
5.2%
1 14968
 
2.9%
space 14968
 
2.9%
detached 11144
 
2.1%
carport 10022
 
1.9%
Other values (9) 35087
 
6.7%

Most occurring characters

ValueCountFrequency (%)
a 583780
23.1%
t 286870
11.3%
249948
9.9%
e 236494
9.3%
d 201517
 
8.0%
g 154034
 
6.1%
o 141721
 
5.6%
c 134406
 
5.3%
n 124972
 
4.9%
r 112057
 
4.4%
Other values (12) 303992
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2228277
88.1%
Space Separator 249948
 
9.9%
Decimal Number 50122
 
2.0%
Math Symbol 1444
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 583780
26.2%
t 286870
12.9%
e 236494
10.6%
d 201517
 
9.0%
g 154034
 
6.9%
o 141721
 
6.4%
c 134406
 
6.0%
n 124972
 
5.6%
r 112057
 
5.0%
s 94729
 
4.3%
Other values (5) 157697
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 27292
54.5%
1 14968
29.9%
3 3917
 
7.8%
4 2501
 
5.0%
5 1444
 
2.9%
Space Separator
ValueCountFrequency (%)
249948
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2228277
88.1%
Common 301514
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 583780
26.2%
t 286870
12.9%
e 236494
10.6%
d 201517
 
9.0%
g 154034
 
6.9%
o 141721
 
6.4%
c 134406
 
6.0%
n 124972
 
5.6%
r 112057
 
5.0%
s 94729
 
4.3%
Other values (5) 157697
 
7.1%
Common
ValueCountFrequency (%)
249948
82.9%
2 27292
 
9.1%
1 14968
 
5.0%
3 3917
 
1.3%
4 2501
 
0.8%
5 1444
 
0.5%
+ 1444
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2529791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 583780
23.1%
t 286870
11.3%
249948
9.9%
e 236494
9.3%
d 201517
 
8.0%
g 154034
 
6.1%
o 141721
 
5.6%
c 134406
 
5.3%
n 124972
 
4.9%
r 112057
 
4.4%
Other values (12) 303992
12.0%

lotsize
Real number (ℝ)

Skewed 

Distinct14672
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20019.57
Minimum0
Maximum9234720
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:02.872341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1690
Q14235
median7501
Q310018
95-th percentile43560
Maximum9234720
Range9234720
Interquartile range (IQR)5783

Descriptive statistics

Standard deviation136791.22
Coefficient of variation (CV)6.8328752
Kurtosis1129.8043
Mean20019.57
Median Absolute Deviation (MAD)3101
Skewness29.07257
Sum5.4043229 × 109
Variance1.8711838 × 1010
MonotonicityNot monotonic
2024-11-26T19:09:03.032787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4235 29315
 
10.9%
8276 25254
 
9.4%
8141 4830
 
1.8%
1860 4422
 
1.6%
11325.6 2551
 
0.9%
13503.6 2514
 
0.9%
10890 2491
 
0.9%
7405 2330
 
0.9%
6098 2325
 
0.9%
12196.8 2152
 
0.8%
Other values (14662) 191768
71.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 126
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
13 3
 
< 0.1%
23 2
 
< 0.1%
ValueCountFrequency (%)
9234720 1
< 0.1%
9147600 1
< 0.1%
8738571.6 1
< 0.1%
8712000 1
< 0.1%
8058600 1
< 0.1%
7840800 1
< 0.1%
7666560 1
< 0.1%
7492320 1
< 0.1%
7013160 1
< 0.1%
6882480 1
< 0.1%

remodeling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
144965 
1
124987 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters269952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

Length

2024-11-26T19:09:03.166878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:03.282766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

Most occurring characters

ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common 269952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 144965
53.7%
1 124987
46.3%

lotsize_was_null
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
201749 
1
68203 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters269952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

Length

2024-11-26T19:09:03.392306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:03.474571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

Most occurring characters

ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

Most occurring scripts

ValueCountFrequency (%)
Common 269952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 201749
74.7%
1 68203
 
25.3%

mean_school_rating
Real number (ℝ)

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0882631
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:03.594406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q13.7
median5
Q36.3
95-th percentile8.3
Maximum10
Range9
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.8871655
Coefficient of variation (CV)0.37088599
Kurtosis-0.64819179
Mean5.0882631
Median Absolute Deviation (MAD)1.3
Skewness0.19116312
Sum1373586.8
Variance3.5613936
MonotonicityNot monotonic
2024-11-26T19:09:03.760482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 16662
 
6.2%
4 15387
 
5.7%
5 14387
 
5.3%
3 13539
 
5.0%
3.3 11658
 
4.3%
6.3 11195
 
4.1%
3.7 10158
 
3.8%
5.3 10029
 
3.7%
4.7 9719
 
3.6%
7 9664
 
3.6%
Other values (77) 147554
54.7%
ValueCountFrequency (%)
1 1461
 
0.5%
1.2 10
 
< 0.1%
1.3 738
 
0.3%
1.4 15
 
< 0.1%
1.5 1329
 
0.5%
1.6 47
 
< 0.1%
1.7 1840
 
0.7%
1.8 199
 
0.1%
1.9 9
 
< 0.1%
2 7201
2.7%
ValueCountFrequency (%)
10 334
 
0.1%
9.8 121
 
< 0.1%
9.7 930
 
0.3%
9.6 11
 
< 0.1%
9.5 462
 
0.2%
9.4 35
 
< 0.1%
9.3 1691
0.6%
9.2 394
 
0.1%
9 3826
1.4%
8.8 384
 
0.1%

mean_school_distance
Real number (ℝ)

Skewed 

Distinct267
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7615354
Minimum0
Maximum1590.8
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:03.910072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.1
median1.7
Q33.1
95-th percentile9.3
Maximum1590.8
Range1590.8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.3346251
Coefficient of variation (CV)1.9317605
Kurtosis35372.754
Mean2.7615354
Median Absolute Deviation (MAD)0.8
Skewness145.15414
Sum745482
Variance28.458225
MonotonicityNot monotonic
2024-11-26T19:09:04.060709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13003
 
4.8%
1.2 12143
 
4.5%
0.8 11324
 
4.2%
1.1 10963
 
4.1%
0.9 10807
 
4.0%
1.4 10693
 
4.0%
1.3 9686
 
3.6%
1.6 9411
 
3.5%
0.7 8630
 
3.2%
0.6 8477
 
3.1%
Other values (257) 164815
61.1%
ValueCountFrequency (%)
0 24
 
< 0.1%
0.1 298
 
0.1%
0.2 1534
 
0.6%
0.3 2595
 
1.0%
0.4 4921
1.8%
0.5 5438
2.0%
0.6 8477
3.1%
0.7 8630
3.2%
0.8 11324
4.2%
0.9 10807
4.0%
ValueCountFrequency (%)
1590.8 1
 
< 0.1%
725.5 4
 
< 0.1%
725.4 1
 
< 0.1%
312.6 1
 
< 0.1%
122.5 1
 
< 0.1%
45.1 1
 
< 0.1%
40.9 1
 
< 0.1%
40.3 1
 
< 0.1%
39.7 182
0.1%
36.3 1
 
< 0.1%

schools_count
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1959015
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-11-26T19:09:04.222375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile8
Maximum65
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.4242074
Coefficient of variation (CV)1.0544117
Kurtosis114.5776
Mean4.1959015
Median Absolute Deviation (MAD)0
Skewness9.4180006
Sum1132692
Variance19.573611
MonotonicityNot monotonic
2024-11-26T19:09:04.362265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 157345
58.3%
8 29780
 
11.0%
2 26507
 
9.8%
5 20114
 
7.5%
4 15744
 
5.8%
6 6496
 
2.4%
1 5304
 
2.0%
7 3286
 
1.2%
24 1524
 
0.6%
10 610
 
0.2%
Other values (14) 3242
 
1.2%
ValueCountFrequency (%)
1 5304
 
2.0%
2 26507
 
9.8%
3 157345
58.3%
4 15744
 
5.8%
5 20114
 
7.5%
6 6496
 
2.4%
7 3286
 
1.2%
8 29780
 
11.0%
9 334
 
0.1%
10 610
 
0.2%
ValueCountFrequency (%)
65 332
 
0.1%
63 466
 
0.2%
61 96
 
< 0.1%
49 188
 
0.1%
24 1524
0.6%
22 393
 
0.1%
21 27
 
< 0.1%
17 1
 
< 0.1%
16 37
 
< 0.1%
15 87
 
< 0.1%

beds_was_null
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0
215837 
1
54115 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters269952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Length

2024-11-26T19:09:04.500808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T19:09:04.612360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 215837
80.0%
1 54115
 
20.0%

Interactions

2024-11-26T19:08:54.335230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.038436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.775874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.623197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.405786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.046051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.127576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.043519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.683754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.320853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.051667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.893608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.451244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.453309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.201262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.900816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.763789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.546404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.233477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.285917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.168487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.824348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.445822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.348470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.023777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.576236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.600164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.336069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.057838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.911498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.686975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.374071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.434519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.293459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.964938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.570825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.473467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.155105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.726876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.727432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.467880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.198097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.045879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.796321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.499042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.559518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.402814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.082690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.695770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.582815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.260002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.861728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.861952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.605101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.353497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.184340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.936908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.633384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.684462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.527777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.203296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.836386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.707764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.389016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.015480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.993078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.730102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.516801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.305782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.061882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.760195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.809432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.652775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.328266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.945708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.817138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.513958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.240409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.107719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.855042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.704262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.435459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.186850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:39.892168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:42.934431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.762101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.455580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.080149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:49.926455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.623337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.384078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.224459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:32.981536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.844850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.578127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.296199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.026713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.059376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.887067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.571027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.291258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.045877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.732662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.508926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.370383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.123830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:34.969823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.707684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.421170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.155701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.231208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:44.996418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.696024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.428301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.177380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.857633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.636468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.494348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.263542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.094791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:36.905899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.530520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.297132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.371802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.137042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.820971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.551790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.306812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:51.982598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.761435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.727688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.385318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.235387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.046497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.655489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.425628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.496773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.246358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:46.945966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.676754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.424143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.091951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:53.886412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.856664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.510287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.360353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.171467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.780462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.555895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.731090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.371330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.055292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.801729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.543026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.201298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.027772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:55.991290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:33.650879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:35.498225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:37.296438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:38.921058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:40.686127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:43.918552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:45.496301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:47.195882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:48.926698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:50.727419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:52.326294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T19:08:54.180254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-26T19:09:04.721223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
LATLNGZIPbathsbedsbeds_was_nullcoolingfireplaceheatinglotsizelotsize_was_nullmean_school_distancemean_school_ratingparkingprivate_poolpropertyTyperemodelingschools_countsqftstatusstoriestargetyear_built
LAT1.000-0.2650.1850.0270.0680.1290.1780.3330.180-0.0050.194-0.357-0.1510.1350.1590.1510.334-0.0260.0390.1160.0520.075-0.168
LNG-0.2651.000-0.934-0.072-0.0990.1500.2820.2370.234-0.0700.2000.192-0.0420.1490.1250.2010.237-0.023-0.1480.1190.102-0.048-0.132
ZIP0.185-0.9341.0000.0460.0740.1450.1930.2890.1840.0330.112-0.1630.0600.1620.1420.1660.3240.0470.1170.107-0.0770.0500.110
baths0.027-0.0720.0461.0000.5170.1560.0250.1290.0390.0780.0720.0390.1460.0660.0800.0600.075-0.0490.5960.0380.1780.3540.252
beds0.068-0.0990.0740.5171.0000.0660.0190.0100.0180.2750.0430.0040.0580.0310.0120.1160.0170.0260.6530.017-0.1240.2150.094
beds_was_null0.1290.1500.1450.1560.0661.0000.3320.0740.4950.0150.1320.0000.0580.4710.1200.6120.1100.1590.2100.6100.0410.0110.050
cooling0.1780.2820.1930.0250.0190.3321.0000.1590.3300.0070.2300.0000.0490.1690.1310.1340.2770.1150.0470.1670.0180.0470.089
fireplace0.3330.2370.2890.1290.0100.0740.1591.0000.1940.0170.2450.0070.0890.2290.0100.1770.0560.0550.2820.0810.0560.1210.145
heating0.1800.2340.1840.0390.0180.4950.3300.1941.0000.0130.2830.0050.0560.2270.1940.1710.3530.1540.0510.2740.0250.0470.103
lotsize-0.005-0.0700.0330.0780.2750.0150.0070.0170.0131.0000.0260.1270.1030.0110.0210.0380.0170.1130.3100.000-0.3170.037-0.018
lotsize_was_null0.1940.2000.1120.0720.0430.1320.2300.2450.2830.0261.0000.0000.1130.2540.0370.4700.1530.0500.2540.1480.1050.0230.240
mean_school_distance-0.3570.192-0.1630.0390.0040.0000.0000.0070.0050.1270.0001.0000.1890.0000.0000.0000.0030.1810.0780.000-0.025-0.0310.259
mean_school_rating-0.151-0.0420.0600.1460.0580.0580.0490.0890.0560.1030.1130.1891.0000.0660.1080.0730.0760.0420.2060.0480.0680.3170.240
parking0.1350.1490.1620.0660.0310.4710.1690.2290.2270.0110.2540.0000.0661.0000.1790.1570.2470.2020.1030.2380.0300.0760.120
private_pool0.1590.1250.1420.0800.0120.1200.1310.0100.1940.0210.0370.0000.1080.1791.0000.1780.1510.1600.0870.2500.1020.1060.179
propertyType0.1510.2010.1660.0600.1160.6120.1340.1770.1710.0380.4700.0000.0730.1570.1781.0000.1150.0730.1650.1860.1060.0730.128
remodeling0.3340.2370.3240.0750.0170.1100.2770.0560.3530.0170.1530.0030.0760.2470.1510.1151.0000.3120.1000.2470.0110.0500.394
schools_count-0.026-0.0230.047-0.0490.0260.1590.1150.0550.1540.1130.0500.1810.0420.2020.1600.0730.3121.0000.0470.256-0.103-0.0260.078
sqft0.039-0.1480.1170.5960.6530.2100.0470.2820.0510.3100.2540.0780.2060.1030.0870.1650.1000.0471.0000.0320.0230.4640.282
status0.1160.1190.1070.0380.0170.6100.1670.0810.2740.0000.1480.0000.0480.2380.2500.1860.2470.2560.0321.0000.0250.0480.083
stories0.0520.102-0.0770.178-0.1240.0410.0180.0560.025-0.3170.105-0.0250.0680.0300.1020.1060.011-0.1030.0230.0251.0000.1780.139
target0.075-0.0480.0500.3540.2150.0110.0470.1210.0470.0370.023-0.0310.3170.0760.1060.0730.050-0.0260.4640.0480.1781.0000.170
year_built-0.168-0.1320.1100.2520.0940.0500.0890.1450.103-0.0180.2400.2590.2400.1200.1790.1280.3940.0780.2820.0830.1390.1701.000

Missing values

2024-11-26T19:08:56.211624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T19:08:56.819230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statuspropertyTypebathsfireplacesqftbedsstoriestargetZIPLATLNGprivate_poolyear_builtheatingcoolingparkinglotsizeremodelinglotsize_was_nullmean_school_ratingmean_school_distanceschools_countbeds_was_null
0activesingle-family3.512900.004.01.0418000.02838735.179251-79.37648902019centralno datano data8276.0015.25.58.00
1for salesingle-family3.001947.003.02.0310000.09921647.686363-117.21668102019no datano datano data5828.0004.01.33.00
3for saletownhouse3.00897.002.02.0209000.01914539.909857-75.19826501920forced aircentralno data680.0001.40.42.00
4activeother2.001507.003.01.0181500.03475928.103908-81.41937802006electriccentralno data4996.0102.33.84.01
5activesingle-family2.501192.983.01.068000.03811535.053329-89.86280601976no datano datano data8750.0002.71.13.01
6activesingle-family2.003588.003.01.0244900.05040143.153169-93.19982001970forced aircentralno data124582.0003.86.46.00
7for salesingle-family3.001930.003.02.0311995.07708029.815894-95.52288502019gascentralattached garage2056.0003.01.13.00
8for salecoop2.001300.003.06.0669000.01135440.768208-73.82740301965no datano dataattached garage75358.8006.71.53.00
9activeother2.003130.003.02.0260000.07706830.007063-95.48836202015centralcentralno data5715.0104.22.95.01
10for salesingle-family3.002839.004.01.0525000.03302826.021034-80.34039711996forced aircentralcarport10270.0107.32.03.00
statuspropertyTypebathsfireplacesqftbedsstoriestargetZIPLATLNGprivate_poolyear_builtheatingcoolingparkinglotsizeremodelinglotsize_was_nullmean_school_ratingmean_school_distanceschools_countbeds_was_null
297816for salesingle-family3.012505.05.02.0384900.04412641.441758-81.85299501950forced aircentraldetached garage9730.0105.71.43.00
297817for salesingle-family2.50950.02.01.0799500.07821229.464611-98.49365311938wallcentralno data3746.0104.01.33.00
297818for salesingle-family3.011792.04.02.0280000.07708029.815894-95.52288501970othercentraldetached garage6599.0102.70.73.00
297819for salesingle-family2.011829.03.01.0171306.03280528.529380-81.40366701962forced aircentral1 space7704.0102.31.33.00
297820activesingle-family2.501895.03.01.0199900.07611032.707831-97.33826501921no datacentralno data7500.0005.01.33.01
297821for salesingle-family2.001841.04.01.0252990.07708929.586959-95.22560102019no datano data2 spaces8276.0016.01.83.00
297822for salecondo3.001417.02.03.0799000.02000138.910353-77.01773902010forced aircentral1 space4235.0013.00.22.00
297824for salecondo3.002000.03.09.0674999.06065741.940293-87.64685701924otherno datanone4235.0014.34.13.00
297825for salesingle-family3.001152.03.02.0528000.01143440.676808-73.77642501950otherno data2 spaces1600.0104.50.62.00
297826for salesingle-family2.001462.03.01.0204900.07821829.490048-98.39713502019electriccentralno data6969.0004.01.83.00